M2 internship - Leveraging and modeling genetic pleiotropy in causal inference
Stage · Stage M2 · 6 mois Bac+5 / Master UR 7537 BioSTM (Biostatistique, Traitement et Modélisation des données biologiques) - Univ. de Paris · Paris (France)
Date de prise de poste : 1 février 2022
statistical genetics pleiotropy post-genomic data causal inference Mendelian randomization Gaussians mixture models
A major transformation in genetics has occurred with the results of Genome-wide associations studies (GWASs). GWASs, which consist in estimating the effect of genome-wide genetic variants on a studied trait, have yielded to the identification of countless genetic variants significantly associated with many complex traits and diseases without being able to pinpoint a causal mechanism in the vast majority of cases. Therefore, many applications and method development have successfully reused the results of GWASs principally to study relationships between traits. One booming field using GWASs summary statistics data is causal inference between traits in the form of Mendelian randomization. The principle of Mendelian randomization is very simple and analogous to randomized control trials where the effects of variant alleles (instead of drug/placebo) are modeled through regression to estimate and test the causal effect of an exposure trait on an outcome trait. Although extremely appealing, Mendelian randomization relies on a strong assumption: the absence of horizontal pleiotropy occurring when a variant has independent effects on both the exposure and the outcome. Pleiotropy tended to be neglected in Mendelian randomization applications. In a stepping-stone paper published in Nature Genetics in 2018, we have shown that horizontal pleiotropy cannot be neglected and occurs in almost 50% of causal relationships, biasing causal estimates and inflating the false discovery rate of causal relationships. This internship will be dedicated to building a comprehensive method to perform Mendelian randomization and infer causality. In addition, we propose to reroute existing methods based on Mixture of Gaussians to infer causality while concomitantly quantifying pleiotropy at the level of the variants which we do believe to be primordial for our understanding of human genetics. Importantly, the full code of the produced methodology and the results on pleiotropy will be made publicly available and highlighted in scientific publications.
References related to the internship
Daniel M. Jordan, Marie Verbanck, and Ron Do. 2019. “HOPS: A Quantitative Score Reveals Pervasive Horizontal Pleiotropy in Human Genetic Variation Is Driven by Extreme Polygenicity of Human Traits and Diseases.” Genome Biology 20 (1): 222. doi:10.1186/s13059-019-1844-7.
Marie Verbanck, Chia-Yen Chen, Benjamin Neale, and Ron Do. 2018. “Detection of Widespread Horizontal Pleiotropy in Causal Relationships Inferred from Mendelian Randomization Between Complex Traits and Diseases.” Nature Genetics, April, 1. doi:10.1038/s41588-018-0099-7.
The successful candidate:
NB: two master 2 internships will start concomitantly in the lab, although the two internships have very different aims and methodological angles, they have a similar background and are part of the PleioMap project, therefore both interns are expected to collaborate.
Scientific environmentThe 6-months Master 2 internship will be supervised by Dr Marie Verbanck who is Assistant Professor (Maître de Conférences) at the Faculté de Pharmacie at Université de Paris within the UR 7537 BioSTM unit (Biostatistique, Traitement et Modélisation des données biologiques). BioSTM's mission is to develop cutting-edge statistical methodologies to answer real-life biological problems with an emphasis on reproducible science and open research.
Procédure : To apply, please send a concise email describing your research interests and experience as well as an up-to-date CV to Marie Verbanck (firstname.lastname@example.org). Name and contact for references will be appreciated.
Date limite : None
Offre publiée le 30 novembre 2021, affichage jusqu'au 28 janvier 2022